Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Bingo Bonus points
1) I should receive at least 10 points for trying PROC Genmod and giving some insight
about the difference of output.
2) I have used Weka Machine learning tool for variable selection, and I should get 10 points
for that.
3) I should get full 20 points for recreating the program in R.
4) I should get 5 points for using Amelia for multiple imitations in R.
5) I am expecting at least 5 points for using SAS macro in my scoring program.
1. INTRODUCTION
This data set contains approximately 8000 records. Each record in the dataset represents a
customer at an auto insurance company. Each record has two target variables
TARGET_FLAG and TARGET_AMT. TARGET_FLAG indicates if the person was in a car crash.
TARGET_AMT, will be zero if the person did not crash their car and greater than zero if the
car is involved in the crash. First we will build the Logistic Regression model to estimate the
probability that a person will crash their car and then a Linear regression model to estimate
the loss in the event of a crash.
2. DATA PREPARATION
In the dataset, there are 23 dependent variables and 2 response variables. The TARGET_FLAG
and TARGET_AMT are response variables. The below table shows the variables data types.
Table 1: Data Dictionary
VARIABLE NAME
TYPE
TARGET_FLAG
Response variable
TARGET_AMT
Response variable
AGE
BLUEBOOK
CAR_AGE
CAR_TYPE
CAR_USE
CLM_FREQ
EDUCATION
HOMEKIDS
HOME_VAL
INCOME
JOB
continuous
continuous
continuous
categorical
categorical
continuous
categorical
continuous
continuous
continuous
categorical
DEFINITION
Was Car in a crash?
1=YES 0=NO
If car was in a crash, what
was the cost
Age of Driver
Value of Vehicle
Vehicle Age
Type of Car
Vehicle Use
#Claims(Past 5 Years)
Max Education Level
#Children @Home
Home Value
Income
Job Category
1
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
KIDSDRIV
MSTATUS
categorical
categorical
MVR_PTS
continuous
OLDCLAIM
PARENT1
RED_CAR
continuous
categorical
categorical
REVOKED
categorical
SEX
TIF
TRAVTIME
URBANICITY
YOJ
categorical
continuous
continuous
categorical
continuous
#Driving Children
Marital Status
Motor Vehicle Record
Points
Total Claims(Past 5 Years)
Single Parent
A Red Car
License Revoked (Past 7
Years)
Gender
Time in Force
Distance to Work
Home/Work Area
Years on Job
After looking at the above table, among all predictors, we have notice that AGE, BLUEBOOK,
CAR_AGE , CLM_FREQ, HOMEKIDS, HOME_VAL, INCOME, MVR_PTS, OLDCLAIM, TIF and
TRAVTIME variable are continuous variables. Rest are categorical variables.
We will examine each variable by creating a histogram as well as tests for normality. I have put
some of the histograms of the variables frequency distribution. I did not put all of histograms
for brevity.
Among the predictor variables, the AGE variable is kind of bell shaped and looks normally
distributed.
2
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
3
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Some variables are kind of continuous but appear to be like categorical variables, such as
CLM_FREQ, HOMEKIDS and MVR_PTS. When looking at these it seems reasonable to pull them
to the categorical side of the analysis by putting into multiple levels, but in my opinion it will be
better, if we continue to use them as continuous variables.
CAR_AGE, HOME_VAL, and OLDCLAIM have very strong presence in the left side of the
histogram.
For example, in case HOME_VAL, the 30% of customer dies not have home and they are renting
the house or they are homeless. It could be due to the way survey might be done. The
skewness could have been result of that.
Also we could have change the HOME_VAL as categorical variable i.e. as OWN_HOME and value
as yes or no. But seeing the continuity at the right side of the histogram of the variable, it will
be good idea to keep it as continuous variable.
Correlation with response variable
Table 2: Correlation
Variable
AGE
Correlation
-0.10322
CAR_AGE
-0.10065
BLUEBOOK
CLM_FREQ
HOMEKIDS
HOME_VAL
INCOME
MVR_PTS
OLDCLAIM
TIF
TRAVTIME
-0.10338
0.2162
0.11562
-0.18374
-0.14201
0.2192
0.13808
0.08237
0.04815
The above table shows the variables correlation with TARGET_FLAG.
From this we see that there are some variables that we would automatically take forward into
model construction, notably anything that exceeds the 0.10 threshold. We are concerned to see
that travel time has such a very low correlation. We'd expect that the concept of exposure to
operation of their vehicle would significantly increase the likelihood of a crash. We will drop
these two variables from the EDA and model building due to weak correlation. We choose to
abandon the use of TIF and YOJ.
4
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Categorical Variables
We have many categorical variables in the Insurance dataset. Some of them has just two
categories such Yes/No or M/F and some of them have more than two categories. For example,
CAR_TYPE and JOB has many levels and SEX and RED_CAR has just two.
I have kept all the categorical variables in the model. We will now code each categorical
variable as a family of dummy variables. I have given below one of the categorical variable as
dummy variable coding.
By default, the dummy variables for JOB are 0. There are some missing values for JOB and those
will have assigned as 0 by default.
JOB_C = 0;
JOB_HM = 0;
JOB_L = 0;
JOB_M = 0;
JOB_P = 0;
JOB_S = 0;
JOB_BC = 0;
if JOB in ('Doctor' 'Clerical' 'Home Maker' 'Lawyer' 'Manager' 'Professional' 'Student' 'z_Blue
Collar') then do;
JOB_D = (JOB eq 'Doctor');
JOB_C = (JOB eq 'Clerical');
JOB_HM = (JOB eq 'Home Maker');
JOB_L = (JOB eq 'Lawyer');
JOB_M = (JOB eq 'Manager');
JOB_P = (JOB eq 'Professional');
JOB_S = (JOB eq 'Student');
JOB_BC = (JOB eq 'z_Blue Collar');
end;
The rest of the categorical variables are coded like above.
3. DATA PREPARATION
There are some missing values which have been imputed. At the same time, we need to create
flags to as indicator variable as we have imputed the data. The below tables shows the missing
values of some of the variables.
Missing values and Imputation
Table 3: Missing values and mean (All data)
5
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Variable
KIDSDRIV
AGE
HOMEKIDS
YOJ
INCOME
HOME_VAL
TRAVTIME
BLUEBOOK
TIF
OLDCLAIM
CLM_FREQ
MVR_PTS
CAR_AGE
Count
8161
8155
8161
7707
7716
7697
8161
8161
8161
8161
8161
8161
7651
Missing
0
6
0
454
445
464
0
0
0
0
0
0
510
Mean
0.1710575
44.7903127
0.7212351
10.4992864
61898.1
154867.29
33.4887972
15709.9
5.351305
4037.08
0.7985541
1.695503
8.3283231
Table 4: Missing values and mean (Categorized on TRAGET_FLAG)
TARGET_FLAG
Variable
N
N Miss
Mean
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
KIDSDRIV
6008
0
0.1393142
AGE
6007
1
45.3227901
HOMEKIDS
6008
0
0.6439747
YOJ
5677
331
10.6718337
INCOME
5673
335
65951.97
HOME_VAL
5665
343
169075.41
TRAVTIME
6008
0
33.0303446
BLUEBOOK
6008
0
16230.95
TIF
6008
0
5.555759
OLDCLAIM
6008
0
3311.59
CLM_FREQ
6008
0
0.6486352
MVR_PTS
6008
0
1.4137816
CAR_AGE
5640
368
8.670922
KIDSDRIV
2153
0
0.2596377
AGE
2148
5
43.3012104
HOMEKIDS
2153
0
0.9368323
YOJ
2030
123
10.0167488
INCOME
2043
110
50641.3
HOME_VAL
2032
121
115256.55
TRAVTIME
2153
0
34.7681203
BLUEBOOK
2153
0
14255.9
TIF
2153
0
4.780771
OLDCLAIM
2153
0
6061.55
CLM_FREQ
2153
0
1.2169066
MVR_PTS
2153
0
2.4816535
CAR_AGE
2011
142
7.3674789
6
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
If we look at the above table, we notice there are several variables, which have missing values.
We notice that of the missing records that none are exceedingly large proportions of the
observed data. There are at the most 5% missing values for some of the variables. We will use
mean to impute the missing value. We are aware that simply imputing with mean can bias the
data but since the number of missing value are low, mean imputation will be fine. We feel that
it is still better than removal of the observations from the data set.
These are the list of Imputed variables and corresponding flag variables listed below.
IMP_AGE and IND_IMP_AGE ;
IMP_CAR_AGE and IND_IMP_CAR_AGE;
IMP_HOME_VAL and IND_IMP_HOME_VAL
IMP_INCOME and IND_IMP_INCOME
The flag variable prefixed with IND_ above should be included as an input variable into the
predictive model because the fact that a variable is missing is often times predictive.
4. BUILD MODELS
I have created the three models to compare and get the best model.
Model 1
I have created this model by selecting variables using Weka Explorer tool. I have used the
CfsSubsetEval Evaluator and GreedyStepwise search algorithm for variable selection.
The tool has selected INCOME, HOME_VAL, JOB and CLM_FREQ as the predictor variables.
I have used only these variables in my logistic model. I made sure that the JOB variables has
dummy variables created and used in the model. Please look at the below
Screenshot which depicts the variable selection from Weka tool.
7
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Based on the above variable selection, I have build the model and the coefficients I got is
depicted below.
The model with the above coefficients and intercept is constructed below.
8
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Log(p/1-p) = -0.8346 -3.02E-06 *IMP_INCOME -2.83E-06 IMP_HOME_VAL +CLM_FREQ 0.3795
-0.2185*JOB_L -0.6173 *JOB_M +JOB_BC *0.4622
The interpretation of the above model, all variables being held, is that: For a one unit increase
in CLM_FREQ there will be a 73% increase in the likelihood of a crash. For a one unit decrease in
the amount of IMP_INCOME there is a 50% decrease in the likelihood of a crash.
The above % value is found using p = exp(x)/(1+exp(x)).
The coefficient relation seems intuitive and reasonable. But the ROC value is bit lower which is
0.6967. At this moment we should look at the other model for better Roc value.
9
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
If we look at the KS output below, The D statistic is the maximum difference between the
cumulative distributions between events (Y=1) and non-events (Y=0). In this model,
D=0.293500.
Higher the value of D, the better the model distinguishes between events and non-events.
10
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Model 2
I have created this model by selecting variables which has higher correlations. Please look at
the Table 2. I have included all the categorical variables.
The tool has selected BLUEBOOK ,CLM_FREQ ,HOMEKIDS ,IMP_INCOME ,MVR_PTS ,OLDCLAIM
,IMP_HOME_VAL and some categorical variables as the predictor variables. These continuous
variables from table 2 which has higher correlations are used here.
Based on the above variable selection, I have build the model and the coefficients I got is
depicted below.
11
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
The model with the above coefficients and intercept is constructed below.
Log(p/1-p) = 0.0362 -0.00002 *BLUEBOOK +0.2059 *CLM_FREQ +0.1271 *HOMEKIDS -3.94E06 *IMP_INCOME +0.1205 *MVR_PTS -0.00001 *OLDCLAIM -1.28E-06 *IMP_HOME_VAL 0.6266 *TYPE_MINI +0.3081 *TYPE_SPOR -0.7158 *USE_P +0.4465 *EDU_HS +0.449
*EDU_ZHS -0.7357 *JOB_M -0.4652 *MARRIED_Y +0.3749 *PARTENT_S +0.9158 *REV_L 2.1897 *R_URBANICITY
The interpretation of the above model, all variables being held, is that: For a one unit increase
in PARTENT_S there will be a 59% increase in the likelihood of a crash. That means being single
parent is risky and more likely to crash. For a one unit increase in the amount of HOME_KIDS
there is a 53% increase in the likelihood of a crash. These sounds intuitive and reasonable.
The above % value is found using p = exp(x)/(1+exp(x)).
12
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
The ROC value is way higher than the Model 1 which is 0.8011.
If we look at the KS output below ,In this model, D=0.45688 which higher than the model 1.
Higher the value of D, the better the model distinguishes between events and non-events.
13
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Model 3
I have created this final model by including all the variables in the data and I wanted the Logistic
Regression Forward selection method choose the variables. Surprisingly the variable TRAVTIME
makes into the selected variable list. If you look at tat the Table 2, the TRAVTIME has very low
correlation but the forward selection method has included in the prediction. I have included all
the categorical variables.
The tool has selected BLUEBOOK ,CLM_FREQ ,HOMEKIDS ,IMP_INCOME ,MVR_PTS ,OLDCLAIM
,IMP_HOME_VAL, TRAVTIME and some categorical variables as the predictor variables.
Based on the above variable selection, I have build the model and the coefficients I got is
depicted below.
14
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
The model with the above coefficients and intercept is constructed below.
Log(p/1-p) = -0.4361 -0.00002 *BLUEBOOK +0.2005 *CLM_FREQ +0.1323 *HOMEKIDS -4.02E06 *IMP_INCOME +0.1191 *MVR_PTS -0.00001 *OLDCLAIM +0.0146 *TRAVTIME -1.26E-06
15
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
*IMP_HOME_VAL -0.6355 *TYPE_MINI +0.3092 *TYPE_SPOR -0.7159 *USE_P +0.4526
*EDU_HS +0.4597 *EDU_ZHS -0.7076 *JOB_M -0.4778 *MARRIED_Y +0.3944 *PARTENT_S
+0.9195 *REV_L -2.3095 *R_URBANICITY
The interpretation of the above model, all variables being held, is that: For a one unit increase
in TRAVTIME there will be a 50% increase in the likelihood of a crash. That means being single
parent is risky and more likely to crash. For a one unit decrease in the amount of BLUEBOOK
value, there is a 50% decrease in the likelihood of a crash. These sounds intuitive and
reasonable.
The above % value is found using p = exp(x)/(1+exp(x)).
The ROC value is the highest in this Model which is 0.8052.
16
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
If we look at the KS output below , in this model, D=0.459944 which is the highest among all
three models.
17
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
5. SELECT MODELS
Model 1
AIC
8677.301
ROC
0.6967
KS (Decile value)
0.293500
Model 2
Model 3
7512.858
7455.178
0.8011
0.8052
0.45688
0.459944
The above table shows the AIC, ROC and KS measures of the all the three models. If we
compare AIC and ROC measure, the Model 3 seems the best model. AIC is the lowest and Roc is
the highest.
KS also captures the discriminatory power of the model in separating “Good” from “Bad”. It is
the highest separation between the Cumulative Good Rate and Cumulative Bad Rate. Higher
the KS, better is the model (higher separation between good and bad). KS values can range
between 0 -100%, KS values greater than 20% are considered acceptable for a model.
18
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
The Model 2 and Model 3 both has KS (D value) value more than 45% and Model 3 KS value is
the highest so we will go for Model 3.
6. Conclusion
We have built the three Logistic Regression Model to best predict who will crash the car based
on the predictor variables. We started with first Model with the help of external tool called
Weka. The two other models were built using seeing correlation and also using SAS forward
selection method. We found that Model 3 is the best model. We have used AIC, ROC and KS
statistic to find the best model. Although the Model 3 has one extra variable (TRAVTIME) than
Model 2. But seeing the result if AIC, ROC and KS, we will choose Model 3.
BINGO BONUS:
PROC GENMOD
proc genmod data=imp_temp descending ;
model TARGET_FLAG = IMP_AGE BLUEBOOK IMP_CAR_AGE CLM_FREQ HOMEKIDS
IMP_INCOME MVR_PTS OLDCLAIM TRAVTIME
IMP_HOME_VAL TYPE_MINI TYPE_PICK TYPE_SPOR TYPE_VAN TYPE_SUV USE_P
EDU_HS EDU_BA EDU_MA EDU_ZHS
JOB_C JOB_HM JOB_L JOB_M JOB_P JOB_S JOB_BC MARRIED_Y PARTENT_S
RED_C REV_L SEX_F R_URBANICITY /dist=binomial link=logit ;
run;
I have used the Proc Genmod and when I compared with Proc Logistic, I found that the result
AIC value is poorer than the Model 3 above. The AIC value for Genmod is 7471.2101.
Also the output of Proc Genmod is not as intuitive than Proc Logistics. By default , the ROC
value is not calculated. The ROC value helps compare the model. It also included long list of
variables.
19
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
Weka
I have downloaded and used Weka Machine Learning tool to select the variables for Model 1.
The screenshot has been attached in the Model 1 section.
R
I have written the R program to create Logistic Regression for TARGET_FLAG and Linear
Regression for TARGET_AMT.
I have used the Amelia package to do the imputation. Amelia uses the Multiple Imputation
technique call EM. Surprisingly, the ROC from R using Amelia is 0.8148. AIC value from R code
using Amelia is 7354 which is the lowest if we compare with Model 1, 2 and 3.
I have used the output of the R and submitted on the Kaggle and got best score.
20
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
21
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
R Code
require(moments)
require(ggplot2)
require(gridExtra)
require(Amelia)
###############################
mydata <read.csv(file.path("/Users/ajha/Documents/411","logit_insurance.csv"),header=T,na.strings=c("") )
mydata$BLUEBOOK <- gsub(",","",mydata$BLUEBOOK)
mydata$BLUEBOOK <- gsub("\\$","",mydata$BLUEBOOK)
mydata$BLUEBOOK <- as.double(mydata$BLUEBOOK)
mydata$INCOME <- gsub(",","",mydata$INCOME)
mydata$INCOME <- gsub("\\$","",mydata$INCOME)
mydata$INCOME <- as.double(mydata$INCOME)
mydata$HOME_VAL <- gsub(",","",mydata$HOME_VAL)
mydata$HOME_VAL <- gsub("\\$","",mydata$HOME_VAL)
mydata$HOME_VAL <- as.double(mydata$HOME_VAL)
mydata$OLDCLAIM <- gsub(",","",mydata$OLDCLAIM)
mydata$OLDCLAIM <- gsub("\\$","",mydata$OLDCLAIM)
mydata$OLDCLAIM <- as.double(mydata$OLDCLAIM)
str(mydata)
mydata$PARENT1 <- as.factor(as.integer(mydata$PARENT1))
mydata$MSTATUS <- as.factor(as.integer(mydata$MSTATUS))
mydata$SEX <- as.factor(as.integer(mydata$SEX))
mydata$EDUCATION <- as.factor(as.integer(mydata$EDUCATION))
mydata$JOB <- as.factor(as.integer(mydata$JOB))
mydata$CAR_USE <- as.factor(as.integer(mydata$CAR_USE))
mydata$CAR_TYPE <- as.factor(as.integer(mydata$CAR_TYPE))
mydata$RED_CAR <- as.factor(as.integer(mydata$RED_CAR))
mydata$REVOKED <- as.factor(as.integer(mydata$REVOKED))
mydata$URBANICITY <- as.factor(as.integer(mydata$URBANICITY))
str(mydata)
22
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
###############################
#Using Amelia to impute missng values using multiple imputations
amelia_fit <- amelia(mydata, m=2, parallel = "multicore", noms = c(
'PARENT1','MSTATUS','SEX','EDUCATION','JOB','CAR_USE','CAR_TYPE','RED_CAR','REVOKED','URBANICIT
Y'))
mydata <- amelia_fit$imputations[[1]]
mydata[1,]
#Create model on trainig set TARGET_FLAG
mod_v1<-glm(TARGET_FLAG~KIDSDRIV + AGE + HOMEKIDS + YOJ + INCOME + HOME_VAL +PARENT1 +
MSTATUS + SEX + EDUCATION+ JOB
+TRAVTIME+ CAR_USE
+BLUEBOOK
+TIF
+CAR_TYPE+ RED_CAR+
OLDCLAIM
+CLM_FREQ+ REVOKED
+MVR_PTS+
CAR_AGE
+URBANICITY, data=mydata,family=binomial(),na.action=na.omit)
mod_v1
predpr <- predict(mod_v1,type=c("response"))
library(pROC)
roccurve <- roc(mydata$TARGET_FLAG ~ predpr)
plot(roccurve)
#Create model on trainig set TARGET_AMT
mydata <- mydata[mydata$TARGET_AMT > 0,]
mod_vlr<-lm(TARGET_AMT~KIDSDRIV + AGE + HOMEKIDS + YOJ + INCOME + HOME_VAL +PARENT1 +
MSTATUS + SEX + EDUCATION+ JOB
+TRAVTIME+ CAR_USE
+BLUEBOOK
+TIF
+CAR_TYPE+ RED_CAR+
OLDCLAIM
+CLM_FREQ+ REVOKED
+MVR_PTS+
CAR_AGE
+URBANICITY, data=mydata)
step(mod_vlr, direction="forward")
############################################
# Load test set data
mydata_test <read.csv(file.path("/Users/ajha/Documents/411","logit_insurance_test.csv"),header=T,na.strings=c("") )
mydata_test$BLUEBOOK <- gsub(",","",mydata_test$BLUEBOOK)
mydata_test$BLUEBOOK <- gsub("\\$","",mydata_test$BLUEBOOK)
mydata_test$BLUEBOOK <- as.double(mydata_test$BLUEBOOK)
mydata_test$INCOME <- gsub(",","",mydata_test$INCOME)
mydata_test$INCOME <- gsub("\\$","",mydata_test$INCOME)
mydata_test$INCOME <- as.double(mydata_test$INCOME)
mydata_test$HOME_VAL <- gsub(",","",mydata_test$HOME_VAL)
mydata_test$HOME_VAL <- gsub("\\$","",mydata_test$HOME_VAL)
23
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
mydata_test$HOME_VAL <- as.double(mydata_test$HOME_VAL)
mydata_test$OLDCLAIM <- gsub(",","",mydata_test$OLDCLAIM)
mydata_test$OLDCLAIM <- gsub("\\$","",mydata_test$OLDCLAIM)
mydata_test$OLDCLAIM <- as.double(mydata_test$OLDCLAIM)
str(mydata_test)
mydata_test$PARENT1 <- as.factor(as.integer(mydata_test$PARENT1))
mydata_test$MSTATUS <- as.factor(as.integer(mydata_test$MSTATUS))
mydata_test$SEX <- as.factor(as.integer(mydata_test$SEX))
mydata_test$EDUCATION <- as.factor(as.integer(mydata_test$EDUCATION))
mydata_test$JOB <- as.factor(as.integer(mydata_test$JOB))
mydata_test$CAR_USE <- as.factor(as.integer(mydata_test$CAR_USE))
mydata_test$CAR_TYPE <- as.factor(as.integer(mydata_test$CAR_TYPE))
mydata_test$RED_CAR <- as.factor(as.integer(mydata_test$RED_CAR))
mydata_test$REVOKED <- as.factor(as.integer(mydata_test$REVOKED))
mydata_test$URBANICITY <- as.factor(as.integer(mydata_test$URBANICITY))
str(mydata_test)
#Using Amelia to impute missng values using multiple imputations
mydata_test$TARGET_FLAG <- mydata_test$TARGET_AMT <- NULL
amelia_fit <- amelia(mydata_test, m=2, parallel = "multicore", noms = c(
'PARENT1','MSTATUS','SEX','EDUCATION','JOB','CAR_USE','CAR_TYPE','RED_CAR','REVOKED','URBANICIT
Y'))
mydata_test <- amelia_fit$imputations[[1]]
mydata_test[1,]
################################################
#Predict for the Test set
mydata_test$TARGET_FLAG <- predict.glm(mod_v1, newdata=mydata_test, type="response")
sub <- subset(mydata_test, select=c("INDEX","TARGET_FLAG"))
names(sub) <- c('INDEX','P_TARGET_FLAG')
write.table(sub,"/Users/ajha/Documents/411/out_glm.csv",sep=',',col.names=T,row.names=F)
mydata_test$TARGET_AMT <- predict.lm(mod_vlr, newdata=mydata_test, type="response")
sub <- subset(mydata_test, select=c("INDEX","TARGET_FLAG","TARGET_AMT"))
names(sub) <- c('INDEX','P_TARGET_FLAG','P_TARGET_AMT')
sub$AMT <- sub$P_TARGET_FLAG*sub$P_TARGET_AMT;
write.table(sub,"/Users/ajha/Documents/411/out_glm_amt.csv",sep=',',col.names=T,row.names=F)
24
Ajay Nath Jha
PREDICT_411-DL_SEC60
Kaggle user: ajayjha2017
SAS Macro
I have used SAS macro in the score part of the code. Please have a look at the score code. The snippet
of the SAS macro is below:
%macro SCORE_FLAG( INFILE, OUTFILE );
data &OUTFILE.;
set &INFILE.;
ODDS_Y = -0.4361 -0.00002*BLUEBOOK + 0.2005*CLM_FREQ + 0.1323*HOMEKIDS 0.00000402*IMP_INCOME +
0.1191*MVR_PTS -0.00001*OLDCLAIM + 0.0146*TRAVTIME 0.00000126*IMP_HOME_VAL 0.6355*TYPE_MINI + 0.3092*TYPE_SPOR -0.7159*USE_P +
0.4526*EDU_HS + 0.4597*EDU_ZHS 0.7076*JOB_M - 0.4778*MARRIED_Y + 0.3944*PARTENT_S +
0.9195*REV_L -2.3095*R_URBANICITY;
P_TARGET_FLAG = exp(ODDS_Y)/(1+exp(ODDS_Y));
P_TARGET_AMT1 = 4131.65436 +
0.11017*BLUEBOOK;
keep index P_TARGET_FLAG P_TARGET_AMT1;
run;
%mend;
%score_flag( temp , temp_out );
proc print data=temp_out;
var INDEX P_TARGET_FLAG;
run;
25